Compare all weighted clustering coefficients side by side

A convenience wrapper around clustering_coef that always computes all five methods and returns them in a single wide data frame, making it easy to identify variables that score consistently high (robust cluster hubs) versus those that vary across methods (structurally ambiguous variables).

Usage

compare_clustering(x, normalize = TRUE)

Arguments

x

A catgraph object.

normalize

Logical. Default TRUE.

Value

A data.frame with columns variable, watts_strogatz, barrat, onnela, zhang, redundancy, and mean_cc (the row mean across all five methods, excluding NAs). Sorted by mean_cc descending.

See also

clustering_coef, plot_clustering

Examples

df <- expand_table(Titanic)
cg <- catgraph(df)
compare_clustering(cg)
#>   variable watts_strogatz barrat    onnela     zhang redundancy   mean_cc
#> 1    Class              1    0.5 0.2861019 0.2530913  1.0000000 0.6078387
#> 2      Sex              1    0.5 0.2795694 0.2482151  0.8088828 0.5673334
#> 3      Age              1    0.5 0.2106029 0.3691475  0.7145339 0.5588569
#> 4 Survived              1    0.5 0.2688767 0.3138760  0.6472822 0.5460070